Machine learning classifiers can predict Gleason pattern 4 prostate cancer with greater accuracy than experienced radiologists

Abstract

Objective The purpose of this study was: To test whether machine learning classifiers for transition zone (TZ) and peripheral zone (PZ) can correctly classify prostate tumors into those with/without a Gleason 4 component, and to compare the performance of the best performing classifiers against the opinion of three board-certified radiologists. Methods A retrospective analysis of prospectively acquired data was performed at a single center between 2012 and 2015. Inclusion criteria were (i) 3-T mp-MRI compliant with international guidelines, (ii) Likert ≥ 3/5 lesion, (iii) transperineal template ± targeted index lesion biopsy confirming cancer ≥ Gleason 3 + 3. Index lesions from 164 men were analyzed (119 PZ, 45 TZ). Quantitative MRI and clinical features were used and zone-specific machine learning classifiers were constructed. Models were validated using a fivefold cross-validation and a temporally separated patient cohort. Classifier performance was compared against the opinion of three board-certified radiologists. Results The best PZ classifier trained with prostate-specific antigen density, apparent diffusion coefficient (ADC), and maximum enhancement (ME) on DCE-MRI obtained a ROC area under the curve (AUC) of 0.83 following fivefold cross-validation. Diagnostic sensitivity at 50% threshold of specificity was higher for the best PZ model (0.93) when compared with the mean sensitivity of the three radiologists (0.72). The best TZ model used ADC and ME to obtain an AUC of 0.75 following fivefold cross-validation. This achieved higher diagnostic sensitivity at 50% threshold of specificity (0.88) than the mean sensitivity of the three radiologists (0.82). Conclusions Machine learning classifiers predict Gleason pattern 4 in prostate tumors better than radiologists. Key Points • Predictive models developed from quantitative multiparametric magnetic resonance imaging regarding the characterization of prostate cancer grade should be zone-specific. • Classifiers trained differently for peripheral and transition zone can predict a Gleason 4 component with a higher performance than the subjective opinion of experienced radiologists. • Classifiers would be particularly useful in the context of active surveillance, whereby decisions regarding whether to biopsy are necessitated.

Automated Classification of Significant Prostate Cancer on MRI: A Systematic Review on the Performance of Machine Learning Applications

“…The flow diagram is depicted in Figure 1 . In total, 27 articles were eligible for inclusion in this review [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ]. From these, 13 studies reported enough information to perform a meta-analysis.…”

Multiparametric MRI for Prostate Cancer Characterization: Combined Use of Radiomics Model with PI-RADS and Clinical Parameters

“…Several previous studies outlined the potential of radiomics for PCa characterization in mpMR images. Antonelli et al [ 18 ] found that zone-specific models combining PSAD and radiomic features outperformed experienced radiologist assessments for the csPCa detection. Chaddad et al [ 23 ] found a significant correlation between GLCM features extracted jointly from T2w and ADC images and Gleason score, with an AUC of 0.78 for GS ≤ 3, 0.82 for GS 3+4 and 0.65 for GS ≥ 4+3.…”

“…Analyzed image features include shape features, first-order statistics, and texture characteristics, such as grey level co-occurrence matrices (GLCM) [ 16 ]. Recent advances in image analysis techniques using radiomic features to assess genomic, proteomic, and clinical phenotypes of the prostate tumor were shown to match or even surpass the qualitative imaging assessment of radiologists [ 17 , 18 , 19 ].…”

<p>Evaluation of Multiple Prognostic Factors of Hepatocellular Carcinoma with Intra-Voxel Incoherent Motions Imaging by Extracting the Histogram Metrics</p>

“…2) With regard to specific cut-off value based diagnosis, diagnostic models such as logistic regression, support vector machine, K nearest neighbor and so on are much better at classifying. 19,21 Previous research has demonstrated the clinical potential of mono-exponential DWI or histogram analysis in evaluating the therapeutic response of HCC (for example: TACE or other novel targeted therapies) 22 and diagnostic value in combination with LI-RADS. 23,25 However, hardly ever were IVIM-derived histogram metrics applied for addressing the above issues.…”